code: def __call__(self, x):
      x = self.l1(x).relu()
      x = self.l2(x).relu()
      x = self.l3(x).relu().dropout(0.8)
      return self.l4(x)
  
file_path: tinygrad/extra/optimization/pretrain_valuenet.py
imports: from tinygrad.codegen.linearizer import Linearizer
  from tqdm import tqdm, trange
  import math
  import random
  from tinygrad.tensor import Tensor
  from tinygrad.nn import Linear
  from tinygrad.nn.optim import Adam
  from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
  from tinygrad.ops import LazyOp, TernaryOps, BinaryOps, UnaryOps, ReduceOps, BufferOps, MemBuffer, ConstBuffer
  from tinygrad.dtype import dtypes
  from tinygrad.shape.shapetracker import ShapeTracker
  from tinygrad.shape.view import View
  from tinygrad.shape.symbolic import Variable
  from tinygrad.codegen.kernel import Opt, OptOps
  from extra.optimization.helpers import lin_to_feats, MAX_DIMS
label_desc: Use image node_id 2 for calling the ValueNet obj's underlying member method code with example usage: obj.__call__(x) and returns: self